Search Results (1,054)

Normal contact stiffness is a key property of mechanical surface joints because it governs load transfer, local approach, vibration response, and positioning stability. Rotary burnishing can generate regular dimple patterns without superimposed tool vibration; however, existing process models mainly describe texture geometry and surface-layer modification rather than the mapping from burnishing parameters to normal joint stiffness. This paper develops a two-level semi-analytical model that couples deterministic dimple geometry and load redistribution with a Greenwood–Williamson-type response of the load-bearing land. The main geometrical and contact contributions are expressed explicitly, whereas the spectral summation, micro-roughness quadrature, and nonlinear closure are evaluated numerically. The process parameters define the nominal unit-cell geometry, whereas the residual dimple dimensions and measured post-burnishing descriptors are post-burnishing inputs. Published data for related regular microreliefs are used to benchmark the predicted trends and order of magnitude. For the reference regime, the explicit spectral displacement remains below 1% of the micro-roughness contribution. Periodic BEM predicts an additional approach 14–15% lower than the analytical term, while pressure heterogeneity changes the averaged micro-roughness approach by less than 1.7%. The model is also used for sensitivity analysis and inverse design of rotary-burnishing parameters to generate design maps for preliminary process selection. Within the stated validity domain, the model serves as a fast screening tool for the stabilized repeated-loading stiffness of dimpled joints. Full article

Koshlyakov’s 1954 paper is an opus magnum containing almost all (a.a.) buds of modular relations, equivalent assertions to the functional equation of Dedekind zeta-functions of the rational and quadratic fields. We shall restore this in the framework of Koshlyakov–Oberhettinger–Soni consisting of zeta-functions corresponding to the gamma factor $\Gamma \left(\frac{s}{2}\right)$ (Riemann), Γ(s) (Hecke) and $\Gamma {\left(\frac{s}{2}\right)}^2$ (Voronoĭ-Bochner) and locate more recent developments in their proper position in the framework. In partic-ular, we elucidate the situation where Ramanujan’s kernels, the sinus cardinalis function, Poisson and Plana summation formulas, etc., take place, thus giving a clue for them to be modular relations. Full article

The Egyptian cotton leafworm Spodoptera littoralis (Boisduval) is a major polyphagous pest in vegetable and cotton crops. Accurate forecasting of its field generations is essential for effective integrated pest management (IPM). Forecasting its generations in the field is traditionally based on the trapping of males, but the use of temperature sums (degree-days, DD) could offer a robust alternative for triggering pest control interventions. The aim of this study was to monitor the flight activity and calculate the summations of degree-days, as well as to compare expected generation peaks and the reliability of S. littoralis males in predicting generations over three successive growing seasons. The study was carried out over three consecutive growing seasons (2017, 2018, and 2019) at Abnob district in Assiut Governorate, Egypt, comparing twice-weekly male captures using pheromone traps and emergence dates predicted by a degree-day model established from physiological data (development threshold: 10.5 °C; thermal constant: 480.6 DD for one generation). Laboratory experiments at four constant temperatures (17, 22, 27, and 32 °C) established the lower developmental thresholds and thermal constants for each life stage: egg (t₀ = 11.86 °C; K = 40.10 DD), larva (t₀ = 7.56 °C; K = 283.76 DD), pupa (t₀ = 12.27 °C; K = 149.50 DD), and pre-oviposition period (t₀ = 12.58 °C; K = 23.96 DD). Six successive generations (observed generations) appeared after the overwintering generation each year, with the overwintering generation consistently initiating in early May each season. The capture dates from the male sex-pheromone traps coincided with the adult emergence dates calculated by the accumulated heat model; deviations between observed (the occurrence of the real peak days) and predicted generation dates ranged from −2 to +2 days in the vast majority of cases, except for in the 2nd generation of the 2019 season, when the observed generation occurred 4 days before the expected generation date. Mean accumulated thermal units per generation ranged from 479.62 to 483.78 DD across the three seasons (grand mean: 481.95 DD), with overall mean seasonal deviations of 0.00, −0.14, and −0.43 days, confirming that the model predicts generation peaks to within ±2 days in the vast majority of cases. The integration of the two methods improves the reliability of phenological forecasting for S. littoralis, opens new opportunities for sustainable pest suppression, and improves the timing of control actions. This combined approach is recommended as a practical decision-support tool for IPM programs targeting S. littoralis in Egypt and comparable Mediterranean agroecosystems. Full article

Accurate classification of volatile organic compounds (VOCs) is important for environmental monitoring and industrial safety via electronic nose (E-nose) systems. However, extracting discriminative features from dynamic one-dimensional sensor responses remains challenging, especially when the recognition model is expected to maintain low computational complexity. This study introduces MSD-GasNet, a lightweight multi-scale depthwise convolutional network combined with Gramian Angular Summation Field (GASF) encoding, for VOC classification using E-nose response signals. The gas-sensing response curves are first transformed into two-dimensional GASF images to preserve temporal correlation information and provide structured inputs for convolutional feature learning. MSD-GasNet further adopts parallel 3 × 3 and 5 × 5 depthwise convolutional branches with feature fusion to capture local response details and broader morphology-related patterns while reducing parameter redundancy. Evaluated on Dataset 1, which contains five representative VOC categories including 1-butanol, acetone, benzaldehyde, butyl acetate, and dimethylbenzene, MSD-GasNet achieves an accuracy of 96.80 ± 0.78%, with 796.6 K parameters and 2.54 ms inference time per sample. Compared with traditional machine learning classifiers, conventional CNN baselines, recent lightweight networks, and a single-scale ablation model, MSD-GasNet shows better classification performance under the current five-class setting. An additional independent validation on Dataset 2 achieves an accuracy of 95.12 ± 1.11% under a chronological train/test split, further supporting the generalization potential of the proposed method. This work provides a GASF-based lightweight multi-scale framework with potential for efficient VOC recognition in portable or resource-limited E-nose applications. Full article

This paper presents a frequency-reconfigurable dual-band phase shifter operating in the n257 (26.5–29.5 GHz) and n260 (37–40 GHz) bands for fifth-generation (5G) communication. The proposed phase shifter is based on an active vector-summing architecture and provides simultaneous gain and phase control for beamforming applications. To support dual-band operation with a large frequency separation, a reconfigurable RC–RL polyphase filter (PPF) is employed for in-phase/quadrature (I/Q) signal generation. The proposed PPF reconfigures its inductance and capacitance according to the operating band, reducing insertion loss and minimizing I/Q phase error in both frequency bands. Gain and phase are controlled by digital-to-analog converter (DAC)-assisted vector summation with 4-bit gain and 6-bit phase control resolution, while a reconfigurable output matching network provides optimized impedance matching in each operating mode. The phase shifter is implemented in a 28 nm fully depleted silicon-on-insulator (FDSOI) process with a core area of 0.26 mm². The measured RMS phase errors are <1.18° and <1.5°, and the RMS gain errors are <0.26 dB and <0.35 dB in the n257 and n260 bands, respectively. The measured DC power consumption is 11 mW and 15.4 mW in the n257 and n260 bands, respectively. Full article

This paper develops a unified qualitative framework for four classes of Liouville–Caputo fractional difference equations arising from different combinations of fractional sums and integer-order differences. Based on the equivalence between initial/final value problems and Volterra-type summation equations, sufficient conditions for the existence and uniqueness of solutions are established by applying the Banach contraction mapping principle together with refined combinatorial estimates. Furthermore, the continuous dependence of solutions on prescribed initial or final data is investigated. By deriving explicit error estimates through a discrete fractional Gronwall-type inequality, we prove that Lipschitz solutions depend continuously on perturbations of boundary data. Numerical experiments for a representative case are presented to verify the theoretical results, including the influence of the fractional order and the sensitivity with respect to boundary data, while additional examples illustrate the applicability of the framework. The obtained results extend the unified discrete fractional calculus framework by providing a rigorous well-posedness analysis and offering a theoretical foundation for further applications of discrete fractional models with memory effects and diverse boundary conditions. Full article

Romania’s Mecanismul de Feedback al Pacientului (MFP; Patient Feedback Mechanism) has collected post-discharge patient experience feedback from public hospitals since 2016 and publishes monthly aggregate workbooks through official channels, but the files are heterogeneous in layout, inconsistently granular, and have never been consolidated into a documented, reusable resource. This data descriptor presents a harmonized, reproducible open dataset built from 117 original workbooks. A Python pipeline integrates them into three analysis-ready artifacts: a national monthly series (113 observed releases over the December 2016–May 2026 span, 6004 tidy answer rows, an 18-row question legend spanning a 2024 instrument revision); a provider release panel (493,450 rows, 466 county plus exact-name provider identities, 42 counties, 39 releases); and a structurally separate legacy 2016 paper-based instrument. Provenance was verified by re-downloading sampled sources and confirming byte-for-byte identity, and national totals reconstructed by hospital summation reconcile exactly with an independent re-aggregation of the published hospital-level details. The release adds SHA-256 source checksums, a machine-readable data dictionary, and a provenance log. The descriptor documents reuse hazards (a ten-to-eight-item instrument break, uneven granularity, aggregate-only records, and string-defined provider identity) and a reuse opportunity: provider- and county-level integrity (informal-payment solicitation) items, usable for rare-event analysis under explicit shrinkage caveats. Full article

The Sombor index has recently become a central tool among degree-based graph invariants; however, it does not explicitly isolate degree imbalance along edges. In this work, we introduce the degree-based Contrastive Sombor Index (CSO), which combines endpoint-degree magnitude with local degree imbalance. For a finite simple graph $G=(V,E)$, the index is defined by $\mathrm{CSO}\left(G\right)={\displaystyle \sum _{uv\in E\left(G\right)}}\left(\sqrt{d{\left(u\right)}^2+d{\left(v\right)}^2}-\sqrt{2}\min \{d\left(u\right),d\left(v\right)\}\right).$ Unlike the Sombor index, which primarily reflects the magnitude of the endpoint degrees, the CSO contribution vanishes when the endpoint degrees are equal and responds to degree imbalance while retaining degree-scale information. In particular, it can distinguish certain graphs having the same total edgewise irregularity but different endpoint-degree distributions. In this work, we first show that $CSO\left(G\right)\ge 0$ and prove that $CSO\left(G\right)=0$ if and only if each connected component of G is regular. We also establish general lower and upper bounds for CSO. In addition, we obtain a relation connecting the CSO index with the first Zagreb index and the edgewise degree differences. We also discuss extremal aspects of the index. As an application, we derive an explicit summation formula for CSO on monogenic semigroup graphs. From our computations on $\Gamma \left(S_M\right)$, it follows that the asymptotic growth order of the index satisfies $CSO(\Gamma \left(S_M\right))=\Theta \left(n^3\right).$ These results show that the CSO index combines degree-magnitude information with sensitivity to unequal endpoint degrees and provides an additional perspective on degree heterogeneity in graphs. Full article

This paper investigates the quasi-synchronization (QS) problem for drive-response discrete-time delayed inertial neural networks (DTDINNS). Unlike existing studies that mainly rely on classical stability theorems, linear matrix inequality (LMI) methods, and matrix measure approaches (MMA), this work establishes three innovative quasi-synchronization criteria for DTDINNS by adopting the solution formula of second-order linear difference equations (SOLDES), infinite series summation techniques, and the solution formula of first-order linear difference equation group (Lemma 6). To the best of our knowledge, this study is the first attempt to introduce the general solution theory of second-order linear difference equations and infinite series summation methods to analyze the synchronization behavior of neural networks (NNS). The proposed framework offers a novel theoretical tool for the synchronization analysis of discrete-time delayed neural networks (DTDNNS), which bears important theoretical significance for relevant research fields. Full article

Background: Stroke is a leading cause of neurological disability worldwide, with upper limb impairment affecting approximately 70% of survivors and only 5–20% achieving complete dexterity recovery at six months. Brain–computer interface (BCI) neurorehabilitation decodes motor intentions from electroencephalographic (EEG) signals to deliver synchronized functional electrical stimulation (FES) and virtual reality feedback, creating a closed-loop neurofeedback system that reinforces motor learning. While existing evidence supports BCI efficacy and safety in chronic stroke, its feasibility, safety, and cost-effectiveness during the acute and subacute phase (2 to 12 weeks post-stroke), when neuroplasticity is heightened, remain underexplored. Furthermore, there is a paucity of data regarding preliminary health economic analyses for BCI rehabilitation in acute stroke rehabilitation settings. Methods: This prospective, open-label, single-arm pragmatic feasibility pilot trial will recruit 12 patients with hemorrhagic or ischemic stroke (2–12 weeks post-stroke) undergoing inpatient rehabilitation from a public healthcare institution. Up to 15 sessions of BCI-rehabilitation of 30 min each using the recoveriX system will be supervised by a trained therapist or clinical research assistant (4–5 sessions/week over 3–4 weeks), followed by standard occupational therapy within 30–60 min of BCI-rehabilitation. Primary outcomes assessing feasibility and adherence include eligibility and recruitment rate (%/screened); tolerability using self-rated System Usability Scale (SUS) score; within-session adherence > 80%/240 trials, summated for completed trials per patient; programme completion number > 80% of scheduled (>12/15) sessions; and training-related adverse events per patient ≤ 17% (≤2/12 sessions). Secondary outcome measures include clinical efficacy by arm impairment scale using hemiplegic Upper Limb Fugl–Meyer Motor Assessment (FMA-UE), hand function using Action Research Arm Test (ARAT), admission and discharge functional status (Functional Independence Measure-FIM (18–126), Modified Barthel Index-MBI (0–100), stroke impact scale (SIS_3.0), arm, participation domains), and economic analysis. All outcomes will be measured by trained therapists/researchers at baseline week 0, week 3–4 (post-BCI-rehabilitation), and week 12 and 24 (follow-up). BCI-rehabilitation EEG-derived electrophysiological correlates of recovery will be extracted to better understand participant progress over time. An incremental cost-utility analysis will compare the BCI-rehabilitation participants against propensity-matched historical controls from the TTSH stroke rehabilitation registry (2017 to 2025), stratified by baseline motor severity. Discussion: This study will provide preliminary evidence on feasibility, tolerability, safety, clinical efficacy, and cost-effectiveness of early BCI-rehabilitation in acute/subacute stroke to better inform clinicians on its implementation. Full article

This paper presents an electronically tunable first-order universal filter capable of operating in multiple analog modes, realized through a compact architecture built around multiple-input operational transconductance amplifiers (MI-OTAs). By leveraging the MI-OTA’s ability to accommodate several input signals within a single transconductance stage—allowing direct arithmetic operations such as summation and subtraction—the proposed design minimizes the number of active elements traditionally required for mixed-mode filtering. Consequently, both inverting and non-inverting forms of low-pass, high-pass, and all-pass responses can be generated in voltage mode, current mode, transadmittance mode, and transimpedance mode, enabling a total of 24 distinct first-order transfer functions using one unified circuit topology. The pole for all responses can be conveniently adjusted by electronically tuning the OTA transconductance. The multiple-input capability is realized using a multi-input MOS technique, while subthreshold-biased bulk-driven transistors allow the circuit to function from a 0.5 V supply with an extended input voltage range and ensure ultra-low power dissipation. The filter was designed and evaluated in Cadence Virtuoso using a 65 nm TSMC CMOS process. Under a 7 nA bias current, the low-pass configuration achieves a consumption of 87.5 nW and a dynamic range of 44.7 dB. Additionally, experimental verification was performed using the commercial LM13700 OTA, confirming the correct operation and practicality of the proposed approach. Full article

Near-field beam shaping for phased-array antennas operating in the Fresnel region is a challenging non-convex electromagnetic synthesis problem, requiring coherent control of the radiated fields while accounting for the distinct positions, radiation patterns, and polarization states of individual array elements. This paper presents a physics-informed optimization framework for near-field beam shaping based on a unified vector formulation that enables the direct coherent summation of the electromagnetic fields radiated by array elements despite their distinct local spherical coordinate systems. Unlike conventional formulations that rely on repeated transformations between local spherical and global Cartesian coordinate systems, the proposed representation preserves the physical polarization properties of the electromagnetic field while providing a rigorous framework for near-field beam synthesis. To optimize the electromagnetic energy distribution over finite target surfaces rather than a single focal point, an analytical near-field point-focusing solution is integrated into the optimization process through a physically informed initialization strategy. The resulting non-convex optimization problem is solved using a genetic algorithm (GA) to determine the element phase distribution that maximizes electromagnetic energy within the prescribed target region while minimizing undesired field leakage. The proposed methodology is validated through full-wave electromagnetic simulations and extensive experimental measurements using a dedicated phased-array platform, including the design, fabrication, characterization, and calibration of the antenna array and phase-control network. The results demonstrate flexible near-field beam shaping and controlled energy focusing over finite target regions. The proposed framework is applicable to biomedical radar sensing, near-field synthetic aperture radar (SAR) illumination, wireless power transfer (WPT), high-power microwave (HPM) systems, and near-field millimeter-wave communications. Full article

Quality deterioration of chilled lamb during storage poses a challenge to meat quality and safety control, making rapid and accurate freshness-grade classification essential. Existing methods based on either spectral information or RGB image information alone are insufficient to simultaneously characterize internal chemical changes and external appearance changes during lamb quality deterioration. To address this issue, this study developed a chilled lamb freshness-grade classification method by integrating deep features from two-dimensional visible–near-infrared (Vis–NIR) spectral encoding with RGB image features. In this method, one-dimensional Vis–NIR spectra were transformed into two-dimensional encoded images using Gramian angular difference field (GADF), Gramian angular summation field (GASF), Markov transition field (MTF), and recurrence plot (RP) to enhance the representation of inter-wavelength structural relationships in spectral sequences, thereby compensating for the limited ability of conventional one-dimensional spectral modeling to capture global correlations and local variation information. Meanwhile, recursive feature elimination (RFE)-selected spectral deep features were fused with Spearman-selected RGB image features to construct a deep–shallow classification model. The results showed that the fusion models outperformed the single-modality models, with GADF(10%)+Image-SVM achieving the best performance, yielding an accuracy, F1-score, and MCC of 0.966, 0.957, and 0.946, respectively. Shapley additive explanations (SHAP) analysis further indicated that GADF deep features were the primary contributors, while RGB image features provided effective complementary information, demonstrating the potential of the proposed method for rapid and nondestructive freshness-grade classification of chilled lamb. Full article

To address class imbalance in transmission line fault traveling-wave samples, the strong non-stationarity of transient traveling-wave features, and the limited identification capability of single-representation methods, this paper proposes a fault type identification method that integrates Polar Lights Optimizer (PLO)-based feature selection with a Gramian Angular Field (GAF) attention fusion network. First, the Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is applied to balance six fault categories in the training set, and time domain, frequency domain, time–frequency domain, and waveform-edge features are extracted from traveling-wave signals acquired by online monitoring devices. Then, PLO is used to select key explicit features, while the preprocessed traveling-wave sequences are encoded into dual-branch images using the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Finally, a Gramian Angular Field–Parallel Convolutional Neural Network–Attention (GAF-PCNN-AT) model is constructed to fuse deep image features with selected explicit features for fault identification. Validation on the independent real test set under a representative stratified 8:2 split shows that the proposed method achieves an accuracy of 95.40% and an average area under the curve (AUC) of 0.9900 in the six-class fault identification task. The results indicate that the proposed method can effectively integrate deep image features of traveling-wave signals with PLO-selected explicit features, thereby providing high identification accuracy and good overall classification performance. Full article

Robust monitoring of weld pool dynamics is critical for automated arc welding; however, single-modality sensors are frequently constrained by severe optical interference and high-frequency environmental noise. To address these limitations, we propose M2WPR-Net, a novel multimodal framework that synergizes visual and acoustic signals for simultaneous weld width regression and physical quality classification. The architecture employs a dual-stream ResNet50 backbone to process heterogeneous sensory data. Specifically, the visual stream utilizes a Convolutional Block Attention Module (CBAM) to suppress intense arc glare and localize the weld pool. Concurrently, the acoustic stream transforms 1D audio sequences into 2D Gramian Angular Summation Field (GASF) textures, which are subsequently refined by Squeeze-and-Excitation (SE) networks to isolate target frequency channels. A central contribution of this study is a bidirectional cross-modal attention mechanism based on Query–Key–Value (Q-K-V) matrix operations. Overcoming the shortcomings of static feature concatenation, this module dynamically aligns the modalities, enabling acoustic cues to guide visual feature extraction and vice versa, thereby mitigating information bottlenecks. Optimized via a joint multi-task loss function, the proposed M2WPR-Net significantly outperforms existing single-modal and conventional fusion baselines. Experimental results demonstrate that the network achieves a Mean Absolute Error (MAE) of 0.18 mm for width prediction and a 93.5% accuracy in penetration state classification, confirming its resilience and practical applicability in complex industrial welding environments. Full article